Sampling and Feature Concatenation: Shou et al. in SCNN [1]} uniformly sampled a fixed number of frames from each proposal and fed them to a visual encoder for feature extraction. This is not computationally efficient because there are many overlapping proposals and overlapping segments are processed multiple times. To address this problem, Gao et al. in Turn-Tap [2]} and CBR [3]} decomposed the video into non-overlapping equal-length units and extracted the features of each unit only once. Different numbers of consecutive units are grouped together at each anchor unit to generate multi-scale proposals. To obtain the proposal features, the features of all units are concatenated. Using this approach, the proposal features are computed from unit-level features, which are calculated only once. However, concatenation of features within each proposal or sampling frames do not lead to rich feature extraction.
